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  • Getting Started
  • User Guide
  • API
  • Examples
  • Contributing
  • GitHub
  • Twitter
  • YouTube
  • Discourse

Section Navigation

  • Extending PyTensor
    • Graph Structures
    • Graph Rewriting
    • Ops
    • Creating a new Op: Python implementation
    • Extending PyTensor with a C Op
    • Adding JAX, Numba and Pytorch support for Ops
    • Overview of the compilation pipeline
    • Types
    • Implementing C support for Types
    • Views and inplace operations
    • Developer documentation for Scan
    • Implementing some specific Ops
    • Using COp params
    • Unit Testing
    • Extending PyTensor: FAQ and Troubleshooting
    • Tips
  • Optimizations
  • Troubleshooting
  • Glossary
  • Links
  • Acknowledgements
  • User Guide
  • Links

Links#

This page lists links to various resources.

PyTensor requirements#

  • git: A distributed revision control system (RCS).

  • pytest: A system for unit testing.

  • numpy: A library for efficient numerical computing.

  • python: The programming language in which PyTensor is written.

  • scipy: A library for scientific computing.

Libraries we might want to look at or use#

This is a sort of memo for developers and would-be developers.

  • autodiff: Tools for automatic differentiation.

  • boost.python: An interoperability layer between Python and C++

  • cython: A language to write C extensions to Python.

  • liboil: A library for CPU-specific optimization.

  • llvm: A low-level virtual machine we might want to use for compilation.

  • networkx: A package to create and manipulate graph structures.

  • pycppad: Python bindings to an AD package in C++.

  • pypy: Optimizing compiler for Python in Python.

  • shedskin: An experimental (restricted-)Python-to-C++ compiler.

  • swig: An interoperability layer between Python and C/C++

  • unpython: Python to C compiler.

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  • Libraries we might want to look at or use
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